Face imaging system for recordal and automated identity confirmation

ABSTRACT

A face imaging system for recordal and/or automated identity confirmation, including a camera unit and a camera unit controller. The camera unit includes a video camera, a rotatable mirror system for directing images of the security area into the video camera, and a ranging unit for detecting the presence of a target and for providing target range data, comprising distance, angle and width information, to the camera unit controller. The camera unit controller includes software for detecting face images of the target, tracking of detected face images, and capture of high quality face images. A communication system is provided for sending the captured face images to an external controller for face verification, face recognition and database searching. Face detection and face tracking is performed using the combination of video images and range data and the captured face images are recorded and/or made available for face recognition and searching.

FIELD OF THE INVENTION

This invention relates to the field of face image recordal and identity confirmation using face images and in particular to the means by which faces can be recorded and identity can be confirmed using face images that are automatically obtained (i.e. without human intervention) in security areas where the movement of people cannot be constrained within defined boundaries.

BACKGROUND OF THE INVENTION

In a world where the prospect of terrorism is an ever increasing threat, there is a need to rapidly screen and record or identify individuals gaining access to certain restricted areas such as airports, sports stadiums, political conventions, legislative assemblies, corporate meetings, etc. There is also a need to screen and record or identify individuals gaining access to a country through its various ports of entry. One of the ways to identify such individuals is through biometric identification using face recognition techniques, which utilize various measurements of a person's unique facial features as a means of identification. Some of the problems associated with using face recognition as a means of rapidly screening and identifying individuals attempting to gain access to a security area are the slow speed of image acquisition, the poor quality of the images acquired, and the need for human operation of such systems.

Attempts to solve these problems in the past have employed a single high-resolution video camera which is used to monitor a security area leading to an entrance. Typically, a fixed focal length lens is employed on the camera. Software is used to analyse the video image to detect and track face images of targets entering the security area. These images are captured, recorded and sent to face recognition and comparison software in an attempt to identify the individuals and verify their right to access the area. One of the main problems with such systems is that the video data is of low resolution and too “noisy” to provide consistently good results. Such systems work reasonably well only when the security area is small and the distances between targets entering the security area and the monitoring camera are relatively constant. Widening the security area and/or trying to accommodate targets at varying distances to the camera, results in some targets having too little resolution in the video image to be properly analysed for accurate face recognition. The main drawback of such systems, therefore, is that they operate successfully only over a very narrow angular and depth range. Captured image quality and therefore the success of face recognition on those images is inconsistent.

Other existing systems use two cameras, one stationary wide field of view camera to monitor the security area and detect faces, and a second, narrow field of view, steerable camera to be pointed, by means of pan, tilt and zoom functions, at the faces identified by the first camera for the purposes of capturing a face image and sending it off for face recognition and comparison to a database. In this method, the second camera is able to obtain high-resolution images necessary for accurate face recognition. The main drawback of these systems is that, as the distance from the first camera increases, it becomes difficult to recognize that a target within the field of view contains a face. Second, the motorized pan, tilt and zoom functions of the second camera are relatively slow. As a result, the system is only capable of tracking one person at a time.

Another solution is to use motorized pan, tilt and zoom cameras, remotely controlled by a human operator to monitor a security area. Such systems are routinely employed to monitor large areas or buildings. A multitude of cameras can be used and normally each operates in a wide-angle mode. When the operator notices something of interest he/she can zoom in using the motorized controls and obtain an image of a person's face for purposes of face recognition. The drawback of such systems is that they require the presence of an operator to detect and decide when to obtain the face images. Such a system is typically so slow that not more than one person can be tracked at a time.

Yet another solution is to require persons seeking entry to a secure area to pass single file, at a restricted pace, through a monitoring area, much the same as passing through a metal detector at an airport. A single, fixed focus camera is set up at a set distance to capture an image of the person's face for face comparison and face recognition. Such a system would severely restrict the flow of persons into the secure area, and in many cases, such as sports stadiums, would be totally unworkable. Moreover, the system would still require an operator to ensure that the camera is pointed directly at the person's face, and do not include any means for ensuring that a proper pose is obtained.

From the above, it is clear that there is a need for an automated face imaging system that overcomes the disadvantages of the prior art by providing the ability to rapidly capture and record high quality face images of persons entering a security area and optionally to make those images available for face comparison and identification. It would be advantageous if such a system included an automated, highly accurate, rapid face detection and face tracking system to facilitate face image capture for the purposes of recordal and/or face comparison and face recognition.

BRIEF SUMMARY OF THE INVENTION

An object of one aspect of the present invention is to overcome the above shortcomings by providing a face imaging system to rapidly detect face images of target persons within a security area and capture high quality images of those faces for recordal and/or for use in face recognition systems for purposes of face verification, face recognition, and face comparison.

An object of another aspect of the invention is to provide a camera system for a face imaging system that is capable of tracking multiple target faces within a security area and providing high quality images of those faces for recordal and/or for use in face recognition systems for purposes of face verification, face recognition, and face comparison.

An object of a further aspect of the invention is to provide a face imaging system that can provide face images of sufficient size and resolution, in accordance with the requirements of known face recognition and face comparison systems, to enable those systems to function at peak efficiency and to provide consistently good results for face identification and comparison.

An object of still another aspect of the invention is to provide a face imaging system that utilizes range data from a ranging unit or other device and video image data to assist in face image detection and face image tracking.

An object of yet another aspect of the invention is to provide a face imaging system that utilizes a historical record of range data from a ranging unit or other device to assist in face image detection and face image tracking.

According to one aspect of the present invention then, there is provided a face imaging system for recordal and/or automated identity confirmation comprising: a camera unit, comprising: a camera unit controller; a video camera for viewing a security area and sending images thereof to the camera unit controller; and a ranging unit for detecting the presence of a target within the security area and for providing range data relating to the target to the camera unit controller, the camera unit controller comprising: a face detection system for detecting a face image of the target; a face tracking system for tracking the face image; a face capture system for capturing the face image when the face image is determined to be of sufficient quality.

The video camera may itself, either wholly or partially, be actuated to effect tracking of a target, for example, by pan, tilt and focus. Or the video camera may view the scene through an actuated reflector means, for example, a mirror, that can rapidly shift the field of view. The pointing of the camera may also be assisted, at least initially, by range data provided by a presence sensor.

The rate of capture of images is based upon the time spent in each of the specific steps of image detection, image tracking and, finally, image capture. The decision to effect image capture is based upon the presence of an image that meets a predetermined quality threshold. Once image capture has occurred, the system is released to repeat the cycle. One object of the invention is to minimize the cycle time.

In preferred embodiments, the face imaging system described herein uses a high resolution, steerable video camera and a high-resolution laser-based rangefinder. The rangefinder scans the monitored security area, typically with a field of view of 45 degrees, approximately every 100 milliseconds and notes the angular locations, distances and widths of any potential targets located therein. The depth of the monitored security area is typically 15 metres but can be modified to suit the particular installation. The angular locations, distances and widths of targets within the monitored security area are presented to a camera unit controller computer that processes the data and sends commands to point the video camera at targets of interest. The commands are for the pan, tilt and zoom functions of the video camera. Based on the distance to the target, the zoom function of the video camera is activated to the degree required to obtain a video image of an average human face filling at least 20% of the image area. Face detection software, assisted by range data specifying the distance, angular location and width of a potential target, is used to analyse the image and determine if it contains a human face. If a face is detected, coordinates of the major face features are calculated and used by the video camera to further zoom in on the face so that it fills almost the entire field of view of the video camera. These coordinates, with reference to the range data and the video image, are constantly updated and can also be used to facilitate the tracking of the target face as it moves about. Once the image quality of the face is determined to be sufficient, according to predetermined criteria based on the face recognition systems being used, face images are captured and recorded and/or made available to face recognition software for biometric verification and identification and comparison to external databases.

The video camera used in the present invention is of a unique design that permits a high speed, accurate pointing operation. The ability of the present invention to rapidly point the video camera enables the tracking of many persons within the security area at the same time in a true multiplexed mode. The video camera is able to point quickly from one person to another and then back again. Unlike other motorized pan, tilt and zoom video cameras, the video camera of the present invention is not moved on a platform to perform the panning operation. Instead, a lightweight mirror is mounted directly on a linear, moving coil, motor and is used to direct an image of a segment of the security area to the video camera. By moving the mirror, the field of view of the video camera can be panned rapidly across the security area in a very brief time, on the order of tens of milliseconds, enabling the system to operate in a true multiplexed mode. Tilting is still performed by moving the video camera itself, but at normal operating distances, the angles over which the video camera must be tilted to acquire a face image are small and can be easily accommodated by existing tilt mechanisms. Zooming is also accomplished in the standard manner by moving the video camera lens elements.

The system of the invention may incorporate image analysis logic that is either “on board” at the location of the camera unit or is situated at a remote location. Thus the camera system can be programed to obtain additional images of special individuals. Face tracking data from the video image may be used to enhance the performance of the face recognition logic operations. Image data can be combined with data from a presence sensor to ensure good lighting and pose control. This can enhance identity confirmation and/or allow the system to maintain a preset standard of consistency.

The benefits of the approach described herein are many. Damage to the video camera is eliminated as it no longer has to be moved quickly back and forth to pan across the security area. Associated cabling problems are also eliminated. No powerful panning motor or associated gears are required to effect the rapid movement of the video camera, and gearing-backlash problems are eliminated. The use of target range data along with target video data allows the system to more accurately detect and track faces in the security area and allows the tracking of multiple target faces. Video and range data is used in a complementary fashion to remove ambiguity inherent in face detection and tracking when only a single source of data is available. Current face recognition software algorithms suffer when the input images are poorly posed, poorly lit, or poorly cropped. The use of target range data in conjunction with target video data allows a more accurate selection of correctly centred images, with good lighting and correct timing of image capture to ensure correct pose. The improved image quality significantly improves face recognition performance.

Further objects and advantages of the present invention will be apparent from the following description and the appended drawings, wherein preferred embodiments of the invention are clearly described and shown.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will be further understood from the following description with reference to the drawings in which:

FIG. 1 is a top-down plan view of the present invention installed within a security area.

FIG. 2 is a side elevation view showing one possible installation position of the video camera, rotatable mirror system, and the ranging unit of the present invention within a security area.

FIG. 3 is a block diagram of the camera unit of the present invention shown in FIG. 1.

FIG. 4 is a block diagram showing the network architecture of the present invention including multiple camera units and an external controller.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS OF THE INVENTION

Referring to FIGS. 1 and 2, an automated identity confirmation system 10 is shown for monitoring targets 1 and obtaining images of target faces 2 entering a security area 4. As also shown in the architecture block diagram in FIG. 4, the automated identity confirmation system 10 comprises one or more camera units 20 and an external controller 50. The camera units 20 include a video camera 21, a rotatable mirror system 25, a ranging unit 30, and a camera unit controller 40.

It will be understood throughout this discussion that security area 4 is a three-dimensional space. The vertical direction is measured from the bottom to the top of the security area in the normal manner, while from the view point of camera unit 20, the horizontal direction is measured from side to side, and the depth is measured from camera unit 20 outward, also in the normal manner. Thus, for a person standing within security area 4 and facing camera unit 20, the vertical direction is from the person's feet to the person's head, the horizontal direction is from the left side of the person to the right, and the depth is from the person's front to back.

Camera Unit—Video Camera

The camera unit 20 includes a standard video camera 21 of the type frequently used in machine vision systems. Although there are a number of camera models, manufactured by different companies, that would be suitable, in the particular instance described herein, the applicant has used a colour video camera manufactured by Sony™, model number EVI-400. This camera features zoom capability, automatic exposure control and automatic focusing. Video camera 21 includes a video output for sending video signals to camera unit controller 40 and a serial input/output (I/O) interface for connecting to camera unit controller 40 to control and monitor the various camera functions such as zoom, focus and exposure. To extend the range over which video camera 21 operates, a teleconverter lens 23 has been added to enable the capture of an image of a human face 2 at a maximum range in such a manner that the face fills the entire video image. In the present instance, the maximum range has been arbitrarily set at 15 meters, however, by increasing the sensitivity of ranging unit 30 and extending the focal length of lens 23, the maximum range can be extended. Camera unit 20 includes a tilt motor 24, and tilt motor driving electronics, for tilting video camera 21 up and down to sweep in the vertical direction. The degree to which video camera 21 needs to be tilted in the vertical direction is small, as it is only necessary to compensate for differences in the vertical height of a person's face from a common reference point, which normally is the average human eye level.

As noted above, camera unit 20 includes focus, tilt and zoom capabilities that permit rapid movement of video camera 21 to acquire high quality face images from target 1. These features are controlled by camera control signals from camera unit controller 40 through the serial interface. Focus on a particular target selected by ranging unit 30 is automatic and merely requires that video camera 21 point to a target. Zoom is controlled to a setting that will initially permit the field of view of video camera 21 to be substantially larger than what an average human face would represent at the target distance. Typically, the zoom is set so that the average human face would fill 20% of the field of view. Zoom is refined by further signals from camera unit controller 40 based on data from ranging unit 30 and video camera 21. In the present setup, the tilt function is provided by external tilt motor 24 mounted to video camera 21, but may in other configurations be incorporated as part of video camera 21. The amount of tilt required to obtain a high quality face image of target 1 is based on data from ranging unit 30 and video camera 21 and is controlled by signals from camera unit controller 40. Range data is important, since target distance is helpful in determining the amount of tilt required.

Where the field of view of video camera 21 is rectangular, having one dimension longer than the other, the applicant has found it advantageous to orient the video camera so that the longer dimension of the field of view is parallel to the vertical direction of security area 4, thus increasing the capture area for vertical targets, such as persons, within security area 4. By increasing the capture area for vertical targets, the applicant reduces the amount of video camera tilt required to obtain a high quality face image of the target.

Camera Unit—Rotatable Mirror System

Camera unit 20 includes a rotatable mirror system 25 located directly in front of video camera 21 as shown in FIG. 1. Rotatable mirror system 25 includes a lightweight mirror 26 mounted directly on a vertical motor shaft 27 of a linear motor 28. Linear motor 28 is of the type used in computer hard drives, and includes servo electronic drivers sufficient to rotate mirror 26 rapidly and accurately to the intended position. Also included, is a standard positional feedback system, mounted directly on shaft 27, comprising circuitry which reads the exact position of mirror 26 and outputs a position feedback signal to the servo drivers. By matching the position feedback signal to a command signal received from camera unit controller 40, representing the intended position of mirror 26, motor 28 can drive mirror 26 to point directly at the intended location.

In the setup shown in FIGS. 1 and 2, ranging unit 30 determines the distance (depth), angular position and width of target 1 within security area 4 and provides those coordinates to camera unit controller 40. Camera unit controller 40 sends a mirror command signal to mirror system 25, to cause linear motor 28 to rotate mirror 26 to the proper location, thus providing a horizontal panning feature for camera unit 20. The image of target 1 incident on mirror 26 is directed to video camera 21 for image capture. By rapidly rotating mirror 26, video camera 21 can be effectively panned across the entire horizontal extent of security area 4 in a fraction of the time it would take a standard video camera, with a motor driven horizontal pan feature to accomplish the same task. The response time is such that panning from any target within security area 4 to any other target within security area 4 can be accomplished in less than 100 milliseconds. Panning accuracy can be attained to within one-tenth of a degree.

Mirror system 25 may include a mirror brake (not shown), which holds and locks mirror 26 in place once the desired target 1 has been acquired. The mirror brake prevents vibrations in mirror 26, thereby improving image stability, and thus enhancing image quality. In a preferred embodiment, the mirror brake is an electromagnet located on shaft 27.

Mirror system 25 could be adapted to include a second degree of rotatable freedom to also provide video camera 21 with a vertical tilt feature, replacing the tilt feature provided by external tilt motor 24. In the alternative, a second rotatable mirror system could be provided that would include a second mirror, rotatable on an axis positioned at 90 degrees to the axis of rotation of mirror 26. In combination, the two rotatable mirror systems would provide video camera 26 with both vertical tilting and horizontal panning features

Camera Unit—Camera/Mirror System Control

Referring to FIG. 3, a camera/mirror system control 39 is connected to video camera 21 and rotatable mirror system 25 and comprises hardware and software components for receiving camera and mirror system control commands from camera unit controller 40 and for controlling the various functions of video camera 21 and mirror system 25. These functions include exposure, zoom, focus, tilt, pan (mirror rotation), on/off, video camera frame rate, brightness and contrast. Camera/mirror system control 39 is also responsible for reading back the status of various video camera 21 and mirror system 25 functions and reporting same to camera unit controller 40.

Camera Unit—Ranging Unit

Referring to FIGS. 1-4, camera unit 20 includes a ranging unit 30 to locate targets 1 within security area 4. In one aspect of the invention, ranging unit 30 is of a common well known design, using a laser diode based distance measuring device operating in conjunction with a rotating range mirror and a range lens receiving system to scan security area 4. A time-of-flight principle is used to calculate the distance to target 1. In the present configuration, the laser diode is pulsed, for a period on the order of 10 nanoseconds, once during every ¼ degree of rotation of the range mirror. The laser beam is reflected by the rotating range mirror into security area 4 and any return pulse reflected by target 1 is measured by the range lens receiving system. Knowing the constant value for the speed of light and the time interval between the emission of the laser pulse and the return reflection, the distance to target 1 can be calculated. Ranging unit 30 records the distance (depth), angular position and width of the detected target within area 4 and sends this information to camera unit controller 40. Because range unit 30 is capable of recording range data for every ¼ degree, range data can provide an target profile that can be analyzed to determine whether it matches the profile of a person (relatively smooth). A complete scan of security area 4 can be accomplished during each rotation of the range mirror which occurs every 100 milliseconds, thus permitting extremely rapid detection and location of targets therein. The scanning rate of area 4 is referred to as the ranging unit frame rate and can be varied according to requirements of the installation or mode of operation.

Ranging unit 30 is generally located below video camera 21 at a level equal to the average person's chest height. Video camera 21 is generally located at the average person's eye level. However, other arrangements for ranging unit 30 and video camera 21 are possible depending on the particular installation.

It will be understood by the reader, that other configurations for ranging unit 30 could be used in the present invention. For example, a sonar-based ranging system could be employed, or one based on high frequency radar or binocular/differential parallax.

Camera Unit—Ranging Unit Control

Ranging unit 30 includes a ranging unit control 41 comprising hardware and software components to manage the various functions of ranging unit 30, including maintaining range mirror rotation speed within specified parameters, regulating laser diode power saving modes, including a “sleep” mode where the laser pulse rate is reduced during “dead” times when there is no activity in the security area, accepting control functions from camera unit controller 40, and sending status information regarding ranging unit 30 to camera unit controller 40 on request. Ranging unit control 41 pre-processes range data by performing various functions including, noise filtering functions to eliminate scattered data comprising single unrelated scan points, moving averaging and scan averaging over multiple scan lines to smooth the range data, sample cluster calculations to determine if detected objects represent targets of interest having the necessary width at a given distance, extracting coordinate information from targets of interest in the form of angle, radius (distance) and width, and building a vectorized profile from the range data of each target. Ranging unit control hardware 41 sends either the raw range data, or the pre-processed, vectorized range data profile to camera unit controller 40 for further processing. The vectorized range data is sent in the form n(a1, r1, w1)(a2, r2, w2) . . . , where n represents the number of targets in the security area scanned, ax represents the angular location of target number x within the security area, rx represents the radius (distance) to target x, and wx represents the width of target x. Range data is sent to camera unit controller 40 on request from range unit control 41 or in a continuous mode at a selectable (programmable) refresh rate.

Camera Unit—Camera Unit Controller

Camera unit 20 also includes a camera unit controller 40 as shown in greater detail in the block diagram of FIG. 3. Camera unit controller 40 includes all of the hardware and software to provide an interface between video camera 21, ranging unit 30, rotatable mirror system 25, and external controller 50. The purpose of camera unit controller 40 is to control the detection, tracking and capture of high quality video images of faces 2 of targets 1 of interest within security area 4. This is accomplished by processing input data received from ranging unit 30 and video camera 21 and using this data to calculate the appropriate pointing command signals to send back to video camera 21 and rotatable mirror system 25. This is described in greater detail below when discussing the various components of camera unit controller 40. Camera unit 30 controller 40 also interfaces with external controller 50 to receive external control commands and send captured video images. External control commands are used both to configure the components of camera units 20 and to modify their behavior, for example, to lock onto and track a particular target within security area 4.

Camera Unit—Camera Unit Controller Hardware

Camera unit controller 40 includes hardware comprising a computer with CPU, RAM, and storage, with interface connections for video input, serial interfaces and high speed I/O, and Ehternet interface. The output from video camera 21 is received on the video input. The output from and control signals to ranging unit 30 are received on one of the serial ports. Control signals for video camera 21 and rotatable mirror system 25 are sent on one of the other of the serial ports. The network interface is used to connect with external controller 50. Other hardware configurations are possible for camera unit controller 40, for example, multiple, low-power CPUs could be used rather than a single high power CPU, the video input from video camera 21 could be a direct digital, or the interface to external controller 50 could be high-speed serial or wireless network, rather than Ehternet.

Camera Unit—Camera Unit Controller Software

Camera unit controller 40 includes camera unit controller software including a modern network capable multi-tasking operating system to control the operation and scheduling of multiple independent intercommunicating software components. The camera unit controller software components include: video camera data processing 43; ranging unit data processing 44; camera/ranging unit control 45; face detection 46; face tracking 47; face image capture 48; camera unit controller system control 49 and camera unit controller communications 60.

Video frames arriving from video camera 21 are asynchronously digitized in a hardware video capture board. This data is presented to video camera data processing 43 which comprises software to perform basic image processing operations to normalize, scale and correct the input data. Corrections are made for image colour and geometry based on standard calibration data. Image enhancement and noise filtering is performed, and the processed video image data is made available to the camera unit controller system control 49 where it is used in performing a number of functions including face detection, face tracking, or face image capture (see below).

Range data arrives at camera unit controller 40 from ranging unit 30 either continuously or in response to a request from camera unit controller 40. The range data takes the form of a table of values of distance (depth or radius), angle and width. The range data is processed by ranging unit data processing 44 which comprises software to determine the position and location of targets 1 within security area 4. Heuristic methods are used to subtract background and remove small diameter “noise”, leaving only larger objects of a size similar to the intended targets, which are persons. These heuristics are intelligent software modules that use historical, probability and statistical analysis of the data to determine the characteristics of objects within the security area. For example, if an object was detected in only one scan of ranging unit 30 and not in the previous or subsequent scans, it can safely be assumed that a spurious event occurred which can be ignored. Similarly, limits can be set on the speed of objects moving in the security area. If an object moved five meters between scans it can safely be assumed that the object is not a person. In addition, calibration data, taken on installation, when security area 4 is totally empty, is used to separate potential targets from fixed objects in the security area, such as support poles and the like (background removal).

The processed range data is made available to camera unit controller system control 49 where it is used to assist in face detection and face tracking. Ranging unit data processing 44 maintains a history buffer of previous range data for each target 1 within security area 4 for a predetermined time interval. The history buffer is used by face detection 46 and face tracking 47 to assist in face detection and face tracking. For example, a single large object may be one large person, or it may be two persons standing close together. If the faces of the two persons are close together it may be difficult to distinguish between the two situations. However, using the history buffer data, it is possible to determine that two single smaller persons were previously separate targets and had moved together. Thus, ambiguous data received from ranging unit 30 and video camera 26 can be clarified.

Camera/ranging unit control 45 comprises software to manage all signals sent via the camera unit controller serial I/O ports to video camera 21, ranging unit 30 and rotatable mirror system 25. These control commands go to ranging unit control 41 and camera/mirror system control 39, and are based on input received from camera unit controller system control 49. Positional changes of the target, based on changes in range data from ranging unit 30 and on changes in the geometric shape of the target video image from video camera 21, are determined by camera unit controller system control 49. Control commands to control video camera on/off; video camera focus; video camera tilt; mirror rotation (panning); video camera zoom; video camera frame rate; video camera brightness and contrast; ranging unit on/off; and ranging unit frame rate, are sent via camera/ranging unit control 45 to facilitate both face detection and face tracking. The purpose of the command signals is to ensure that the target is properly tracked and that a high quality video image of the target's face is obtained for the purpose of face recognition. In addition, camera/ranging unit control 45 manages the appropriate timing of commands sent out, ensures reliable delivery and execution of those commands, alerts camera unit controller system control 49 of any problems with those commands or other problem situations that might occur within video camera 21, ranging unit 30 or rotatable mirror system 25. For example, if rotatable mirror system 25 is not responding to control commands it will be assumed that motor 28 is broken or mirror 26 is stuck and an alarm will be sent out to signal that maintenance is needed.

Face detection 46 comprises software to detect face images within the video image arriving from video camera 21. Initially, face detection 46 uses the entire input video image for the purpose of face detection. A number of different, known software algorithmic strategies are used to process the input data and heuristic methods are employed to combine these data in a way that minimizes the ambiguity inherent in the face detection process. Ambiguity can result from factors such as: variations of the image due to variations in face expression (non-rigidity) and textural differences between images of the same persons face; cosmetic features such as glasses or a moustache; and unpredictable imaging conditions in an unconstrained environment, such as lighting. Because faces are three-dimensional, any change in the light distribution can result in significant shadow changes, which translate to increased variability of the two-dimensional face image. The heuristics employed by face detection 46 comprise a set of rules structured to determine which software algorithms are most reliable in certain situations. For example, in ideal lighting conditions, bulk face colour and shape algorithms will provide the desired accuracy at high speed. Range data from ranging unit 30 is added to narrow the search and assist in determining the specific areas of the video image most likely to contain a human face based on target width and historical movement characteristics of targets within security area 4.

The following are some of the software algorithms, known in the field, that are used by the applicant in face detection:

-   -   Bulk face colour and shape estimation;     -   Individual face feature detection (for eyes, nose, mouth, etc.)         using geometrical constraints to eliminate improbable features;     -   Artificial neural network analysis based on training the         algorithm on a large set of face and non-face data; and     -   Bayesian analysis using Principle Component Analysis (PCA), or         Eigenface decomposition of the face image.

The following additional steps are performed by face detection 46 of the present invention, which utilize range data from ranging unit 30 and have been found by the applicant to increase the ability of the present invention to detect a face within the video image:

-   -   Analysis of the range data to isolate person-size targets. As         discussed above, this includes intelligent software modules         using historical, probability and statistical analysis of the         range data to determine the characteristics of objects within         the security area and to eliminate noise resulting from small or         fast moving objects that are not likely persons. Range data from         ranging unit 30 can be used to determine targets of an         appropriate width (30 cm to 100 cm) and shape (smooth front         surface). Knowing exactly where the person-size target is         located within the video image provides a starting point for         commencing face detection.     -   Analysis of the range data history to determine the presence of         groups of people. This is done by isolating person-sized targets         in each video frame using the above-described technique based on         an analysis of the range data. Motion estimation software, such         as Kalman filtering, is used to estimate the trajectory of such         targets and identify ambiguous targets as those fitting poorly         to the Kalman trajectory estimation. Finally, ambiguous targets         are classified and the classification is used to assist in face         detection. For example, it will be possible to determine whether         a particular ambiguity is the result of two or more persons         standing close together.

In a preferred embodiment of the invention, face detection 46 identifies an image as corresponding to a face based on colour, shape and structure. Elliptical regions are located based on region growing algorithms applied at a coarse resolution of the segmented image. A colour algorithm is reinforced by a face shape evaluation technique. The image region is labelled “face” or “not face” after matching the region boundaries with an elliptical shape (mimicking the head shape), with a fixed height to width aspect ratio (usually 1.2).

In a further preferred embodiment of the invention, a method of eye detection using infrared (IR) illumination can be used to locate the eyes on a normal human face and thus assist in face detection 46. In this method, the target is illuminated with bursts of infrared light from an IR strobe, preferably originating co-axially or near co-axially with the optical axis of video camera 21. The IR increases the brightness of the pupil of the human eye on the video image. By locating these areas of increased brightness, face detection 46 is able to quickly identify and locate a potential face within the video image. If the IR strobe is flashed only during specific identified video frames, a frame subtraction technique can be used to more readily identify areas of increased brightness, possibly corresponding to the location of human eyes.. Accurately identifying the location of the eyes has a further advantage, in that such information can greatly improve the accuracy of facial recognition software.

Face detection is intrinsically a computationally intensive task. With current processor speeds, it is impossible to perform full-face detection on each arriving video image frame from video camera 21. Therefore, the face detection process is only activated by camera unit controller system control 49 when required, that is when no face has been detected within the arriving image. Once a face is detected, face detection is turned off and face tracking 47 takes over. The quality of face tracking 47 is characterized by a tracking confidence parameter. When the tracking confidence parameter drops below a set threshold, the target face is considered lost and face detection resumes. When the tracking confidence parameter reaches a predetermined image capture threshold face images are acquired by face image capture module 48. Once a sufficient number of high quality face images are acquired, the target is dropped and face detection resumes on other targets.

Once a face is detected within the video image, face tracking 47, comprising face tracking software, is activated and processes data input from video camera data processing 43 and ranging unit data processing 44 for the purpose of determining the rate and direction of movement of the detected face, both in the vertical, horizontal and depth directions. Face tracking 47 is initialized with the detected target face position and scale and uses a region-of-interest (ROI) limited to the surrounding bounding box of the detected target face. Any movement is reported to camera unit controller system control 49 where it is used to direct the panning of rotatable mirror system 25 and the zoom, focus and tilting functions of video camera 21, so as to track the target face and keep it within the field of view. The target face is tracked until the tracking confidence drops below a set threshold. In this case the target is considered lost, and the system switches back to detection mode. Camera unit controller system control 49 will determine when to activate face image capture 48.

Face tracking 47 uses a number of known software algorithmic strategies to process the input video and range data and heuristic methods are employed to combine the results. The heuristics employed comprise a set of rules structured to determine which software algorithms are most reliable in certain situations. The following are some of the software algorithms, known in the field, that are used by the applicant in face tracking:

-   -   Frame to Frame differencing to detect movement;     -   Optical flow techniques on the video stream;     -   Bulk face colour and shape estimation;     -   Kalman filter analysis to filter present movement and predict         future movement from past movement estimation; and     -   Artificial neural network analysis based on training the         algorithm on a large set of video sequences.

The following additional step is performed by face tracking 47 of the present invention, which utilizes range data from ranging unit 30 and has been found by the applicant to increase the ability of the present invention to track a face:

-   -   Analysis of range data and range data history. As detailed         above, a history buffer of previous range data for each target         can be used to determine whether a single large object is one         large person, or two persons standing close together, or         possibly not a person at all.

In a preferred embodiment of the invention, an elliptical outline is fitted to the contour of the detected face. Every time a new image becomes available, face tracking 47 fits the ellipse from the previous image in such a way as to best approximate the position of the face in the new image. A confidence value reflecting the model fitting is returned. The face positions are sequentially analyzed using a Kalman filter to determine the motion trajectory of the face within a determined error range. This motion trajectory is used to facilitate face tracking.

Many of the face tracking algorithms rely in part on colour and colour texture to perform face tracking. Due to changes in both background and foreground lighting, image colour is often unstable leading to tracking errors and “lost targets”. To compensate for changes in lighting conditions, a statistical approach is adopted in which colour distributions over the entire face image area are estimated over time. In this way, assuming that lighting conditions change smoothly over time, a colour model can be dynamically adapted to reflect the changing appearance of the target being tracked. As each image arrives from video camera 21, a new set of pixels is sampled from the face region and used to update the colour model. During successful tracking, the colour model is dynamically adapted only if the tracker confidence is greater than a predetermined tracking threshold. Dynamic adaptation is suspended in case of tracking failure, and restarted when the target is regained.

Face tracking 47 is activated by camera unit controller system control 49 only when face detection 46 has detected a face within the video image, and the system operating parameters call for the face to be tracked. These operating parameters will depend on the individual installation requirements. For example, in some situations, a few good images may be captured from each target entering the security area. In other situations, certain targets may be identified and tracked more carefully to obtain higher quality images for purposes of face recognition or archival storage.

Face image capture 48 comprises image capture software which analyses data received from video camera 21 and ranging unit 30 to determine precisely when to capture a face image so as to obtain high quality, well lit, frontal face images of the target. Face image capture 48 uses heuristic methods to determine the pose of the face and best lighting. The correct pose is determined by identifying key face. features such as eyes, nose and mouth and ensure they are in the correct position. Lighting quality is determined by an overall analysis of the colour of the face.

In a preferred embodiment, video camera 21 is provided with a programmable spot metering exposure system that can be adjusted in size and location on the video image. Once a face image is located, the spot metering system is adjusted relative to the size of the face image and is centered on the face image. The result is a captured face image that is correctly exposed and more suitable for image analysis and facial recognition and comparison.

Face image capture 48 is activated by camera unit controller system control 49 when a face has been detected by face detection 46, and the system operating parameters call for a face image to be captured. Parameters affecting image capture include: the number of images required, the required quality threshold of those images, and the required time spacing between images. Image quality is based on pose and lighting and is compared to a preset threshold. Time spacing refers to the rapidity of image capture. Capturing multiple images over a short period does not provide more information than capturing one image over the same time period. A minimum time spacing is required to ensure enough different images are captured to ensure that a good pose is obtained. Once a high quality face image is obtained, it is sent to external controller 50.

The characteristics of the final captured image are determined in large part by the particular face recognition software algorithms being used. One of the main advantages of the present invention is the ability to adjust system operating parameters to provide high, consistent quality face images so as to achieve accurate and consistent face recognition. For example, it is known that certain face recognition software requires a frontal pose, a minimum pixel resolution between the eyes, and a particular quality of lighting. The present invention can be programmed to only capture images which meet this criteria, and to track a given face until such images are obtained, thus ensuring consistent high quality performance of the face recognition system.

Camera unit controller 40 includes a camera unit controller communication system 60 that interfaces via a network connection to connect camera unit controller 40 to external controller 50 to receive configuration and operating instructions or to send video images or data as requested by external controller 50.

The following types of configuration and operating instructions are accepted by camera unit controller communications system 60:

-   -   Configuration of parameters for face detection, face tracking         and face image capture, such as, how long to follow each target,         the number of images to capture, the quality and resolution of         images required, the time spacing of images, and how many         targets to follow;     -   Calibration instructions to determine the necessary image         correction for lighting conditions within the security area;     -   Instructions to capture calibration data for ranging unit 30,     -   Configuration instructions giving the spatial positioning of         camera 21 and ranging unit 30;     -   Operating mode instructions to turn on/off, go into “sleep”         mode, or go into various operational tracking modes. “Sleep”         modes for various components can be useful to extend component         life and save power. For example, ranging unit 30 can be         instructed to reduce its laser pulse rate to one area scan per         second once activity in the security area ceases for a certain         period of time. As soon as a target is detected, ranging unit 30         will “wake up” and commence normal scanning. This can         significantly extend the life of the laser diode.

Various configurations of camera unit controller communication system 60 are possible. Camera units 20 could intercommunicate amongst themselves; camera units 20 could accept commands from and send data to computers other than external controller 50. Additionally, different communications infrastructure could be used, such as point to point networks, high speed serial I/O, token ring networks, or wireless networking, or any other suitable communication system.

Camera unit controller system control 49 comprises software that overseas all functions within camera unit controller 40. All data acquired by video camera data processing 43, ranging unit data processing 44 and camera unit controller communications system 60 are made available to the camera unit controller system control 49 which determines which of the face detection 46, face tracking 47, or face image capture 48 software modules to activate. These decisions are based on particular system requirements such as for example, the number of images required, image quality threshold and image time spacing. Also taken into consideration is the particular operating mode. For example, in one operating mode, only the closest target is followed. In another operating mode, the closest three targets may be followed for three seconds in turn. Operating modes are completely programmable and depend on the particular application.

Camera unit controller system control 49 also determines what commands to send to video camera 21, rotatable mirror system 25, and ranging unit 30 to control their various functions. Additionally, any exceptional modes of operation, such as responding to system errors, are coordinated by camera unit controller system control 49.

Camera unit controller system control 49 combines information from face detection 46 (that indicates the image area is likely a face), with tracking information from face tracking 47 (that indicates the image area belongs to a target that is moving like a person), and with range data from ranging unit data processing 44 (that indicates the image area is the shape of a single person), to select which pixels in the video image are likely to be occupied by faces. To do this, the range data must be closely registered in time and space with the video data. Face tracking accuracy is increased by using a probabilistic analysis that combines multiple measurements of face detection information, face tracking information and range data over time.

Camera unit controller system control 49 used a combination of range and image data, to build a motion history file, storing the trajectories of individual targets within security area 4.

This permits the tracking of individual face targets and the capture of a pre-determined number of face images per person.

External Controller

FIG. 4, is a block diagram showing the network architecture of the present invention. Multiple camera units 20 are shown connected to external controller 50. Also shown are database/search applications 70 and external applications 80 connected via a network interface. FIG. 4 shows the communication and data flow between the various components of the invention. It will be appreciated that the invention does not require that there be a single network connection between all components. Indeed, many security applications require the use of separate networks for each application. The use of multiple camera units 20 will allow for cooperation between camera units to accomplish tasks such as following a target from one security area to another, or covering a large security area with many potential targets.

External controller 50 comprises a computer with network connectivity to interface with camera units 20, database/search applications 70, and external applications 80, which can provide searching of stored face images and additional sources of data input to the system. For example, an external passport control application can provide images of the data page photograph to external controller 50, which can be combined and compared with images captured from camera units 20 to conduct automatic face recognition to verify that the face image on the passport corresponds to the face image of the person presenting the passport.

External controller 50 includes software comprising a modern network capable multi-tasking operating system that is capable of controlling the operation of multiple independent intercommunicating software components, including: camera unit interface 51; external system control 52; search interface 53; camera configuration application interface 54; and external applications interface 55. All network communications are secured using advanced modern network encryption and authentication technologies to provide secure and reliable intercommunications between components.

Camera unit interface 51 includes software that controls communications with camera unit controllers 40. Commands are accepted from external system control 52 and sent to camera units 20. Camera unit interface 51 ensures reliable delivery and appropriate timing of all such communications. Face images arriving from camera units 20 are stored and sequenced to be further processed by other software modules within external controller 50.

External system control 52 includes software that oversees all functions of external controller 50. All data acquired by camera unit interface 51, search interface 53, camera configuration application interface 54, and external applications interface 55, are made available to external system control 52. Any activities that require coordination of camera units 20 are controlled by external system control 52. Additionally, any exceptional modes of operation, such as responding to system errors, are coordinated by external system control 52.

Search interface 53 includes software that provides an interface between external controller 50 and database/search applications 70, as will be described below, ensuring reliable delivery and appropriate timing of all communications therebetween.

Camera configuration application interface 54 includes software that accepts data input from a camera configuration application. A camera configuration application may be located on external controller 50 or on another computer located externally and connected via a network. Camera configuration data is used to send commands to camera units 20 to control various operational and configuration functions, such as exposure, colour mode, video system, etc., to instruct camera units 20 to take calibration data, or shift into operational mode and commence following a specific target.

External applications interface 55 includes software that provides an interface between external controller 50 and external applications 80, as will be described below, ensuring reliable delivery and appropriate timing of communications therebetween.

Database/Search Applications

Database /search applications 70 is a general term use to describe all of the various search functions that can inter-operate with the present invention. These applications accept data from external controller 50, and possibly from other data sources, such as passport control applications, to perform searches, and return a candidate list of possible matches to the input data.

Examples of database search applications include, but are not limited to:

-   -   Face Verification: a captured face image received from camera         unit 20 is compared to a face image taken from a presented         identification document, such as a passport or other picture         identification document. Face recognition and comparison         software is engaged to determine whether or not there is a match         and the results are returned for a report.     -   Face Identification: face images received from camera units 20         are compared against an alert or “look out” list, containing         undesirables. A candidate list of zero or more possible matches         is returned for a report.     -   Database search: identification data from an identification         document such as name, identification number, gender, age, and         nationality is compared against an alert list. A candidate list         of zero or more possible matches to the alert list is returned         for a report.         External Applications

External applications 80 is a general term used to describe other possible security identification systems that are monitoring the same targets or security area as the present invention described herein. Data from external applications 80 can be input to the present system to enhance functionality. It will be appreciated that the details of the interaction between the present invention and external applications 80 will depend on the specific nature of the external applications.

One example of an external application is a passport control system. Travellers present identification documents containing identification data and face images to passport control officers. Identification data and face images from the identification documents are input through external controller 50 to provide enhanced functionality, especially in database/search applications. For example, an image of the traveller obtained from the identification documents can be compared to images of the traveller captured by camera unit 20 to ensure a match (verification). In another example, identification data from the identification document such as gender, age, and nationality can be used to filter the candidate list of face images returned by a face recognition search of the captured face image from camera unit 20 against an alert database.

Additionally, external controller 50 can send information gathered from camera units 20 to external applications 80 to allow enhanced functionality to be performed within these applications. For example, face images captured from camera units 20 can be sent to a passport control application to provide the passport control officer with a side-by-side comparison with the face image from a traveller's identification document. In another example, face images from camera units 20 can be used to allow database search applications to begin processing prior to presentation of identification documents to a passport control officer.

Setup and Calibration

Referring to FIG. 2, in a typical installation, ranging unit 30 is setup to scan security area 4 horizontally at approximately chest height for the average person. Video camera 21 and rotatable mirror system 25 are positioned at approximately eye level for the average person so that the field of view of video camera 21 covers security area 4. The exact positions of ranging unit 30, video camera 21, and rotatable mirror system 25 are accurately measured and their positions within security area 4 are input to camera unit controller 40 as calibration data. Optionally, as shown in FIG. 4, many camera units 20 can be used to cover a large security area, or multiple related areas can be monitored. Depending on the nature of the installation and application requirements, adjustments may be required to the mode of operation and the intercommunication protocols between the various system components.

Ranging unit 30 is calibrated by obtaining and storing range data from security area 4 containing no transient targets. Subsequently, range data obtained during operation is compared to the calibration data to differentiate static objects from transient targets of interest. Video camera 21 provides sample images of known targets under existing operating light conditions. These images allow calibration of face detection 46 and face tracking 47.

Operation

In operation, ranging unit 30 continuously scans monitored security area 4 to detect the presence of targets. Range data, comprising the angular position, distance and width of any potential targets, is transmitted to camera unit controller 40. Camera unit controller 40 processes the range data and identifies targets most likely to be persons based on the location of the targets (closest first), size (person size) and movement history. Once a target is identified for closer inspection, commands are sent by camera controller unit 40 to video camera 21 and mirror system 25 causing them to execute pan and zoom functions so as to obtain a more detailed view of the target. These commands cause mirror 26 to rotate so the target is brought into the field of view of video camera 21 and the zoom of video camera 21 is activated in accordance with the measured distance so that the average human face will fill 20% of the field of view. Face detection 46 is engaged and uses data obtained from the video image combined with the range data to execute face detection algorithms to determine if the image from video camera 21 contains a human face. If a human face is detected, face features are extracted and the spacial coordinates of the centre of the face are calculated. This location information is passed back to camera unit controller system control 49 enabling it to send refined pan (mirror rotation), tilt and zoom commands to video camera 21 and mirror system 25 to cause the detected face to fully fill the video image.

Normally, at this point, camera unit controller 40 will initiate a face tracking mode, to follow the person of interest by using the range and video data to calculate the appropriate pan, zoom, and tilt commands that need to be issued to keep video camera 21 accurately pointed at the target's face and to maintain the desired face image size. While tracking the target, heuristic methods are used to determine appropriate moments to capture high quality, frontal-pose images of the target's face. Also, considered are preset image quality threshold, the number of images required, and the time spacing between images. Once obtained, the images are sent to external controller 50 via a network connection. At this point, camera unit controller 40 will either continue to follow the target, or will shift its attention to tracking another target of interest that may have entered within security area 4, as determined by the application specific work flow logic.

External controller 50 receives the captured video face images and target movement information from each camera unit 20. It also receives information from external applications 80 such as passport control software that may be monitoring the same target persons. As noted briefly above, one example of external information is a photo image captured from an identification document presented by a target person. External controller 50 interfaces with face recognition and other database search software to perform verification and identification of target persons.

Additionally, external controller 50 can coordinate operation between multiple cameras units 20 to enable the following functions:

-   -   1) Tracking of a single person of interest as they pass from one         monitored area to another.     -   2) Coordination of multiple cameras units 20 monitoring a single         room. In this situation, targets of interest are identified and         to allow the various camera units 20 for face tracking and face         image capture.         Other Applications

In addition to the above-described applications, other applications of the present invention include, but are not limited to:

-   -   1. Capture the face image of a person receiving an         identification document such as a passport or visa and storing         that image in a database for use at a later time when         machine-assisted identity confirmation is required to verify the         identity of the person who presents the identity document.     -   2. Perform a “lookout check” on any person applying for an         official identity document by capturing face images of the         person and sending those images to a database search application         for face recognition, identification and comparison to a         database of undesirables.     -   3. Capture face images of the person picking up an identity         document and comparison to the face image of the person on the         identity document, to verify that the document is issued to the         rightful holder.     -   4. Capture and store in a database the face image of a person         when that person receives approval to travel to or enter a         country. Capturing of such face images may or may not be based         on a risk profile. The database is then used to compare with         face images recorded from detained uncooperative persons, or         unauthorized persons entering certain security areas, to         determine if the person has been seen before and if so, what         identity documents were presented at that time.     -   5. Capture and store in a database the face images of persons         checking in for air travel to create an Advance Passenger         Information (“API”) database. API records are sent to         authorities at the arriving destination where they are used to         perform advance lookout checks before the flight arrives to         identify any persons who ought to be subject to detailed         inspection upon arrival.     -   6. Use API data gathered in the above example to support         automated inspection of passengers at the arrival destination.         Face images of arriving passengers are captured and compared to         API data to ensure that those persons arriving are the same         persons who boarded the plane. This allows a rapid deplaning         process where passengers can literally walk-through a security         area and those that ought to be subject to detailed inspection         can be easily identified and selected.     -   7. Capture face images of persons boarding any public         transportation, such as planes, trains or buses, or when         attempting to enter any security area including ports of entry         into countries or sports stadiums, and sending those images to a         database search application for face recognition, identification         and comparison to a database of undesirables, to prevent such         undesirables from using the public transportation, or entering         the security area.     -   8. Capture face images of persons checking in for public         transportation and comparing those images to a face image         contained on an identity document presented during check-in, to         verify that the correct person is presenting the identity         document.     -   9. Capturing face images of persons upon approach to a country's         port of entry inspection area and sending those images to a         database search application for face identification and         comparison to a database of undesirables to assist the         inspection authority in determining whether the person         approaching ought to be allowed entry.     -   10. Capture face images of persons being processed at         self-service inspection machines upon arrival to a country's         port of entry and sending those images to a database search         application for face identification and comparison to a database         of undesirables to prevent entry of such persons into the         country.     -   11. Capture face images of all arriving passengers at all         arrival gates and store those images in an arrivals database         along with the arriving flight details. Use the arrivals         database to compare with face images obtained from persons who         appear at inspection counters without proper identification and         who refuse to supply flight arrival details. This allows border         control authorities to identify the airline and the origin of         the person so that the airline can be fined and forced to carry         the detained person back to the point of origin.     -   12. Perform a “lookout check” on any person entering within any         security area by capturing face images of the person and sending         those images to a database search application for face         identification and comparison to a database of undesirables and         alerting security.     -   13. Improve airline check-in procedures by capturing face images         of passengers as they approach various security areas and         comparing those images to face images of booked passengers. For         example, the face image of the traveller can be obtained upon         initial booking, or at check-in, and used to verify the identity         of the person entering other security areas within the airport         and eventually boarding the plane. This can greatly increase the         speed of check-in and boarding.     -   14. Face images of persons boarding a plane can be compared to         face images of persons at check-in to verify that the person who         checked in is the same person who boarded the plane and to match         that person to luggage loaded on the plane.     -   15. Face images taken continuously by multiple camera units 20         located in many security areas throughout a given location, such         as an airport, can be used to locate any person at any given         time. In this way, a passenger who fails to show up for a flight         can be located and directed to the appropriate boarding area.         Flight delays necessitated to located the wayward passenger can         be reduced. Such monitoring systems can also be valuable in a         prison environment to locate prisoners.     -   16. In situations involving financial transactions, such as at         automated bank teller machines (ATM), captured face images can         be used to compare against data from the ATM card to verify that         the correct person is using the card.     -   17. Capture face images of all persons entering a security area         for comparison to a database/search application, to ensure that         the person is on a pre-approved list of persons permitted entry.

The above is a detailed description of particular preferred embodiments of the invention. Those with skill in the art should, in light of the present disclosure, appreciate that obvious modifications of the embodiments disclosed herein can be made without departing from the spirit and scope of the invention. All of the embodiments disclosed and claimed herein can be made and executed without undue experimentation in light of the present disclosure. The full scope of the invention is set out in the claims that follow and their equivalents. Accordingly, the claims and specification should not be construed to unduly narrow the full scope of protection to which the present invention is entitled. 

1. A face imaging system for recordal and/or automated identity confirmation comprising: a camera unit, comprising: a camera unit controller; a video camera for viewing a security area and sending images thereof to said camera unit controller; and a ranging unit for detecting the presence of a target within said security area and for providing range data relating to said target to said camera unit controller, said camera unit controller comprising: a face detection system for detecting a face image of said target; a face tracking system for tracking said face image; a face capture system for capturing said face image when said face image is determined to be of sufficient quality.
 2. The face imaging system of claim 1, wherein said camera unit includes a rotatable mirror system for reflecting said security area images, said images of said target and said face images into said video camera.
 3. The face imaging system of claim 1, including a camera unit communications system for sending said captured face images to an external controller for purposes of face verification and/or face recognition.
 4. The imaging system of claim 1, wherein said face detection system uses said range data to assist in detecting said face images.
 5. The imaging system of claim 4, wherein said range data includes a distance, an angular location and a width of said target.
 6. The imaging system of claim 1, wherein said face tracking system uses said range data to assist in tracking said faces images.
 7. The imaging system of claim 6, wherein said range data includes a distance, an angular location and a width of said target. 